Fast Algorithms for Packing Proportional Fairness and its Dual
Authors: Francisco Criado, David Martinez-Rubio, Sebastian Pokutta
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we present a distributed accelerated first-order method for this problem which improves upon previous approaches. We also design an algorithm for the optimization of its dual problem. Both algorithms are width-independent. 2. If you are including theoretical results... (a) Did you state the full set of assumptions of all theoretical results? [Yes] (b) Did you include complete proofs of all theoretical results? [Yes] |
| Researcher Affiliation | Academia | Francisco Criado* TU Berlin Berlin, Germany criado@math.tu-berlin.de David Martínez-Rubio* Zuse Institute Berlin and TU Berlin Berlin, Germany martinez-rubio@zib.de Sebastian Pokutta Zuse Institute Berlin and TU Berlin Berlin, Germany pokutta@zib.de |
| Pseudocode | Yes | Algorithm 1 Accelerated descent method for 1-Fair Packing; Algorithm 2 Optimization of the dual of 1-fair packing with oracle O; Algorithm 3 Feasibility oracle O |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | This is a theoretical paper that does not involve empirical studies with datasets. |
| Dataset Splits | No | This is a theoretical paper that does not involve empirical studies with datasets, and therefore no training/validation/test splits are discussed. |
| Hardware Specification | No | This is a theoretical paper that does not describe experimental hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper and does not include details about an experimental setup with hyperparameters or system-level training settings. |